MDS analysis
Goal: Do an MDS plot using Irene’s SNPs to look for relatedness among plants
Snp calling with freebayes
First, need to call SNPs using all BAM files at once:
working in directory 2019/IreneSnps on whitney
first, sort bams
for f in SNPanalysis/*LT*rmdup.bam
do
newname=`basename $f .bam`_sort.bam
samtools sort -o $newname --reference SNPanalysis/Pinsaporeference1 $f
donenext, assign read groups to bams
input=""
for f in `ls *sort.bam`
do
rg=`basename $f _rmdup_sort.bam`
input="$input -b $f -r $rg -s $rg"
done
echo $input
bamaddrg $input > LT_rmdup_sort_combined.bam
samtools index LT_rmdup_sort_combined.bamfreebayes -f SNPanalysis/Pinsaporeference1 --no-indels --no-mnps --no-complex LT_rmdup_sort_combined.bam > LT.vcf &try parallel
ulimit -n 4000
/usr/local/stow/freebayes/scripts/fasta_generate_regions.py Pinsaporeference1.fai 100000 > regions
./freebayes-parallel regions 8 -f Pinsaporeference1 --no-indels --no-mnps --no-complex LT_rmdup_sort_combined.bam > LT.vcf (note: I edited the freebayes-parallel script so that it would work…)
Freeybayes parallel takes about 12 hours
try parallel, keep snps
ulimit -n 4000
/usr/local/stow/freebayes/scripts/fasta_generate_regions.py Pinsaporeference1.fai 100000 > regions
./freebayes-parallel regions 8 -f Pinsaporeference1 --no-mnps --no-complex LT_rmdup_sort_combined.bam > LT_w_indel.vcf scp whitney.plb.ucdavis.edu:2019/IreneSnps/LT.vcf.gz ../input/now analyze vcf in R
library(tidyverse)[30m── [1mAttaching packages[22m ───────────────────────────────────────────────── tidyverse 1.2.1 ──[39m
[30m[32m✔[30m [34mggplot2[30m 3.0.0 [32m✔[30m [34mpurrr [30m 0.2.5
[32m✔[30m [34mtibble [30m 1.4.2 [32m✔[30m [34mdplyr [30m 0.7.6
[32m✔[30m [34mtidyr [30m 0.8.1 [32m✔[30m [34mstringr[30m 1.3.1
[32m✔[30m [34mreadr [30m 1.1.1 [32m✔[30m [34mforcats[30m 0.3.0[39m
[30m── [1mConflicts[22m ──────────────────────────────────────────────────── tidyverse_conflicts() ──
[31m✖[30m [34mdplyr[30m::[32mfilter()[30m masks [34mstats[30m::filter()
[31m✖[30m [34mdplyr[30m::[32mlag()[30m masks [34mstats[30m::lag()[39mlibrary(ggrepel)get the vcf header
vcf.header <- system("zgrep '#C' ../input/LT.vcf.gz",intern = TRUE)
vcf.header[1] "#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\t38LTR\t42LTR\t42LTRR\t43LTR\t43LTRR\t49LTWR\t49LTWRR\t95LTWR\t95LTWRR\t99LTWR"vcf.header <- vcf.header %>%
str_replace("#","") %>% #get rid of the pound sign
str_split(pattern = "\t") %>% #split on the tabs
magrittr::extract2(1)
vcf.header [1] "CHROM" "POS" "ID" "REF" "ALT" "QUAL" "FILTER" "INFO"
[9] "FORMAT" "38LTR" "42LTR" "42LTRR" "43LTR" "43LTRR" "49LTWR" "49LTWRR"
[17] "95LTWR" "95LTWRR" "99LTWR" get the data
snps <- read_tsv("../input/LT.vcf.gz", na = c("","NA","."),comment="#",col_names = vcf.header) %>%
select(-ID, -FILTER) # these are empty columnsParsed with column specification:
cols(
CHROM = col_character(),
POS = col_integer(),
ID = col_character(),
REF = col_character(),
ALT = col_character(),
QUAL = col_double(),
FILTER = col_character(),
INFO = col_character(),
FORMAT = col_character(),
`38LTR` = col_character(),
`42LTR` = col_character(),
`42LTRR` = col_character(),
`43LTR` = col_character(),
`43LTRR` = col_character(),
`49LTWR` = col_character(),
`49LTWRR` = col_character(),
`95LTWR` = col_character(),
`95LTWRR` = col_character(),
`99LTWR` = col_character()
)
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|===========================================================================| 100% 398 MBsnpsfilter to keep snps where there is data from all samples
snps <- snps %>%
filter({select(., matches("[0-9]")) %>% complete.cases() })
snpssnps <- snps %>%
mutate(TOTAL_DEPTH= {str_extract(INFO, "DP=[0-9]*") %>%
str_remove("DP=") %>%
as.numeric() }
) %>%
filter(QUAL >=100,
nchar(ALT)==1,
TOTAL_DEPTH > quantile(TOTAL_DEPTH, 0.05),
TOTAL_DEPTH < quantile(TOTAL_DEPTH, 0.95))
snpsunpack the information differnet samples:
samples <- colnames(snps) %>% str_subset("^[0-9]")
for (s in samples) {
snps <- snps %>%
separate(!!s, into=paste(s,c("gt","tot.depth","allele.depth","ref.depth","ref.qual","alt.depth","alt.qual","gt.lik"),sep="_"),
sep=":", convert = TRUE)
}
snpsFor the MDS we only need the genotype info
gts <- snps %>%
select(ends_with("_gt"))
gtsconvert this to numeric
geno.numeric <- gts %>%
lapply(factor) %>% # convert charcters to "factors", where each category is internally represented as a number
as.data.frame() %>% # reformat
data.matrix() %>%# convert to numeric
t()
head(geno.numeric[,1:5],10) [,1] [,2] [,3] [,4] [,5]
X38LTR_gt 2 2 2 2 2
X42LTR_gt 2 3 2 2 2
X42LTRR_gt 2 2 2 2 1
X43LTR_gt 2 2 2 2 2
X43LTRR_gt 2 2 2 2 2
X49LTWR_gt 2 2 2 2 2
X49LTWRR_gt 3 3 3 3 1
X95LTWR_gt 2 3 3 2 2
X95LTWRR_gt 2 3 3 2 2
X99LTWR_gt 2 2 2 2 2dim(geno.numeric)[1] 10 134421dim(gts)[1] 134421 10genDist <- as.matrix(dist(geno.numeric))
dim(genDist)[1] 10 10#perform the multi-dimensional scaling
geno.mds <- as.tibble(cmdscale(genDist))
geno.mds$ID <- rownames(geno.numeric)
geno.mds <- geno.mds %>%
mutate(ID={str_replace(ID, "W", "N") %>%
str_replace("RR", "R2") %>%
str_remove_all("(X|_gt)") },
response=ifelse(str_detect(ID,"N"), "no recovery", "recovery"))
geno.mdsgeno.mds %>%
ggplot(aes(x=V1, y = V2, label=ID, color=response)) +
geom_point() +
geom_text_repel(show.legend=FALSE, direction="y")
with LTR_38 removed
convert this to numeric
geno.numeric <- gts %>%
select(-`38LTR_gt`) %>%
lapply(factor) %>% # convert charcters to "factors", where each category is internally represented as a number
as.data.frame() %>% # reformat
data.matrix() %>%# convert to numeric
t()
head(geno.numeric[,1:5],10) [,1] [,2] [,3] [,4] [,5]
X42LTR_gt 2 3 2 2 2
X42LTRR_gt 2 2 2 2 1
X43LTR_gt 2 2 2 2 2
X43LTRR_gt 2 2 2 2 2
X49LTWR_gt 2 2 2 2 2
X49LTWRR_gt 3 3 3 3 1
X95LTWR_gt 2 3 3 2 2
X95LTWRR_gt 2 3 3 2 2
X99LTWR_gt 2 2 2 2 2dim(geno.numeric)[1] 9 134421dim(gts)[1] 134421 10genDist <- as.matrix(dist(geno.numeric))
dim(genDist)[1] 9 9#perform the multi-dimensional scaling
geno.mds <- as.tibble(cmdscale(genDist))
geno.mds$ID <- rownames(geno.numeric)
geno.mds <- geno.mds %>%
mutate(ID={str_replace(ID, "W", "N") %>%
str_replace("RR", "R2") %>%
str_remove_all("(X|_gt)") },
response=ifelse(str_detect(ID,"N"), "no recovery", "recovery"))
geno.mdsgeno.mds %>%
ggplot(aes(x=V1, y = V2, label=ID, color=response)) +
geom_point() +
geom_text_repel(show.legend=FALSE, direction="y")
ggsave("../output/MDS.pdf")Saving 7.29 x 4.51 in image
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